Note
Go to the end to download the full example code
How to add new bundles into pyAFQ (Acoustic Radiations Example)#
pyAFQ is designed to be customizable and extensible. This example shows how you can customize it to define a new bundle based on a definition of waypoint and endpoint ROIs of your design. In this case, we add the acoustic radiations.
We start by importing some of the components that we need for this example and fixing the random seed for reproducibility
import os.path as op
import plotly
import numpy as np
from AFQ.api.group import GroupAFQ
import AFQ.api.bundle_dict as abd
import AFQ.data.fetch as afd
from AFQ.definitions.image import ImageFile, RoiImage
np.random.seed(1234)
Get dMRI data#
We will analyze one subject from the Healthy Brain Network Processed Open Diffusion Derivatives dataset (HBN-POD2) 1, 2. We’ll use a fetcher to get preprocessed dMRI data for one of the >2,000 subjects in that study. The data gets organized into a BIDS-compatible format in the ~/AFQ_data/HBN folder:
study_dir = afd.fetch_hbn_preproc(["NDARAA948VFH"])[1]
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Define custom BundleDict object#
The BundleDict object holds information about “include” and “exclude” ROIs, as well as endpoint ROIS, and whether the bundle crosses the midline. In this case, the ROIs are all defined in the MNI template space that is used as the default template space in pyAFQ, but, in principle, other template spaces could be used.
The ROIs for the case can be downloaded using a custom fetcher which saves the ROIs to a folder and creates a dictionary of paths to the ROIs:
ar_rois = afd.read_ar_templates()
bundles = abd.BundleDict({
"Left Acoustic Radiation": {
"start": ar_rois["AAL_Thal_L"],
"end": ar_rois["AAL_TempSup_L"],
"cross_midline": False,
},
"Right Acoustic Radiation": {
"start": ar_rois["AAL_Thal_R"],
"end": ar_rois["AAL_TempSup_R"],
"cross_midline": False
}
})
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Define GroupAFQ object#
HBN POD2 have been processed with qsiprep 3. This means that a brain mask has already been computer for them. As you can see in other examples, these data also have a mapping calculated for them, which can also be incorporated into processing. However, in this case, we will let pyAFQ calculate its own SyN-based mapping so that the combine_bundle method can be used below to create a montage visualization.
For tractography, we use CSD-based probabilistic tractography seeding extensively (n_seeds=4 means 81 seeds per voxel!), but only within the ROIs and not throughout the white matter. This is controlled by passing “seed_mask”: RoiImage() in the tracking_params dict. The custom bundles are passed as bundle_info=bundles. The call to my_afq.export_all() initiates the pipeline.
brain_mask_definition = ImageFile(
suffix="mask",
filters={'desc': 'brain',
'space': 'T1w',
'scope': 'qsiprep'})
my_afq = GroupAFQ(
bids_path=study_dir,
preproc_pipeline="qsiprep",
participant_labels=["NDARAA948VFH"],
output_dir=op.join(study_dir, "derivatives", "afq_ar"),
brain_mask_definition=brain_mask_definition,
tracking_params={"n_seeds": 4,
"directions": "prob",
"odf_model": "CSD",
"seed_mask": RoiImage(use_endpoints=True)},
bundle_info=bundles)
my_afq.export_all()
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Interactive bundle visualization#
Another way to examine the outputs is to export the individual bundle figures, which show the streamlines, as well as the ROIs used to define the bundle. This is an html file, which contains an interactive figure that can be navigated, zoomed, rotated, etc.
bundle_html = my_afq.export("indiv_bundles_figures")
plotly.io.show(bundle_html["NDARAA948VFH"]["Left Acoustic Radiation"])
References#
- 1
Alexander LM, Escalera J, Ai L, et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017;4:170181.
- 2
Richie-Halford A, Cieslak M, Ai L, et al. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Scientific Data. 2022;9(1):1-27.
- 3
Cieslak M, Cook PA, He X, et al. QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nat Methods. 2021;18(7):775-778.
Total running time of the script: (34 minutes 20.189 seconds)
Estimated memory usage: 4175 MB